Title:Drug Repurposing: Translational Pharmacology, Chemistry, Computers and the Clinic
Volume: 13
Issue: 18
Author(s): Naiem T. Issa, Stephen W. Byers and Sivanesan Dakshanamurthy
Affiliation:
Keywords:
Drug discovery, network pharmacology, translational pharmacology, chemoinformatics, drug repositioning, drug
repurposing, bioinformatics, clinical informatics, phenotypic screening, high throughput screening.
Abstract: The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side
effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug
discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer
drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery
has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known
compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been
very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline
in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of
successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate
and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors
while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics,
bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious
drugs. We further discuss the implications of combining these approaches into an integrative methodology for
high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently
approved drugs back into clinical trials for possible new indications.
